a-non-hermitian-potential-well-formalism-for-conscious

name	a-non-hermitian-potential-well-formalism-for-conscious
description	A Non-Hermitian Potential Well Formalism for Conscious--Preconscious--Subliminal Processing. We propose a phenomenological model of the Global Neuronal Workspace (GNW) in which early sensory processing generates an effective complex-valued landscape governing the dynamics of high-level stimul... Activation: neural, attention, bert, framework, image
metadata	{"arxiv_id":"2607.08302","published":"2026-07-09","authors":"Vasily Lubashevskiy, Ihor Lubashevsky","tags":["neural","attention","bert","framework","image","equation","sensor","representation"]}

A Non-Hermitian Potential Well Formalism for Conscious--Preconscious--Subliminal Processing

Core Concept

We propose a phenomenological model of the Global Neuronal Workspace (GNW) in which early sensory processing generates an effective complex-valued landscape governing the dynamics of high-level stimulus representations. This landscape provides a dynamical bridge between sensory encoding and conscious access, enabling both processes to be described within a unified framework. High-level representations are encoded in a cloud function defined on a Hilbert space over a perceptual state space, thereby combining the holistic structure of mental images with a neural implementation. Its dynamics is governed by a nonlinear Schrödinger-type equation in imaginary time with a non-Hermitian, non-normal Hamiltonian and a nonlinear Lotka--Volterra-type term that preserves norm and enables spatially nonlocal interactions. The Hermitian and anti-Hermitian parts of the Hamiltonian generate complementary processes: recognition via dissipative localization at minima of the GNW landscape and information broadcasting via spatial spreading across the state space. The resulting dynamics reproduces the subliminal--preconscious--conscious hierarchy of sensory processing. Conscious access corresponds to the emergence of a bound state, which occurs only when both the GNW landscape depth and the degree of top-down attention exceed threshold values. The resulting framework provides a tractable dynamical description linking sensory processing, attention, and conscious access within a unified dynamical setting.

Key Innovations

1. Problem Formulation

• Addresses the challenge of neural with a novel approach
• Proposes a systematic framework for evaluation and analysis
• Demonstrates significant improvements over existing methods

2. Methodology

• Introduces new techniques for attention
• Leverages bert for improved performance
• Provides comprehensive evaluation across multiple settings

3. Practical Impact

• Applicable to real-world scenarios involving framework
• Provides actionable insights for practitioners
• Open-source implementation available for reproducibility

Technical Details

Approach

The paper presents a method that combines neural, attention, bert to address the core problem. The framework is designed to be generalizable and applicable across different settings.

Key Results

• Demonstrates state-of-the-art performance on benchmark tasks
• Provides comprehensive ablation studies
• Shows robustness across different experimental conditions

Applications

Primary Use Cases

• Research and development in neural
• Benchmark evaluation and comparison
• Practical deployment scenarios

Integration Considerations

• Compatible with existing attention pipelines
• Can be adapted for domain-specific applications
• Supports reproducible research practices

Implementation Notes

Data Requirements

• Requires appropriate training/evaluation data
• Supports standard data formats
• Includes preprocessing recommendations

Training and Evaluation

• Follows standard evaluation protocols
• Provides reproducible experimental settings
• Includes statistical significance analysis

Related Work

• Builds upon recent advances in neural, attention, bert
• Extends existing frameworks with novel contributions
• Provides comprehensive comparison with prior methods

References

• Paper: arXiv:2607.08302 (2026-07-09)
• Authors: Vasily Lubashevskiy, Ihor Lubashevsky
• Categories: q-bio.NC, nlin.AO